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data <- read.csv("Live.csv")
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
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##     intersect, setdiff, setequal, union

library(tidyr)
library(lattice)
library(MASS)

## 
## Attaching package: 'MASS'

## The following object is masked from 'package:dplyr':
## 
##     select

library(lubridate) 

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## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
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library(mdsr)

## Loading required package: ggformula

## Loading required package: ggplot2

## Loading required package: ggstance

## 
## Attaching package: 'ggstance'

## The following objects are masked from 'package:ggplot2':
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##     geom_errorbarh, GeomErrorbarh

## 
## New to ggformula?  Try the tutorials: 
##  learnr::run_tutorial("introduction", package = "ggformula")
##  learnr::run_tutorial("refining", package = "ggformula")

## Loading required package: mosaicData

## Loading required package: Matrix

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## Attaching package: 'Matrix'

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## Registered S3 method overwritten by 'mosaic':
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## 
## The 'mosaic' package masks several functions from core packages in order to add 
## additional features.  The original behavior of these functions should not be affected by this.
## 
## Note: If you use the Matrix package, be sure to load it BEFORE loading mosaic.
## 
## Have you tried the ggformula package for your plots?

## 
## In accordance with CRAN policy, the 'mdsr' package 
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## the 'tidyverse' package automatically.
## You may need to 'library(tidyverse)' in order to 
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library(scales)

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## Attaching package: 'scales'

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##     rescale

library(psych)

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## Attaching package: 'psych'

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library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ tibble  3.0.3     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ✓ purrr   0.3.4

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#library(GGally)

# View(data)

summary(data[,4:12])

##  num_reactions     num_comments       num_shares        num_likes     
##  Min.   :   0.0   Min.   :    0.0   Min.   :   0.00   Min.   :   0.0  
##  1st Qu.:  17.0   1st Qu.:    0.0   1st Qu.:   0.00   1st Qu.:  17.0  
##  Median :  59.5   Median :    4.0   Median :   0.00   Median :  58.0  
##  Mean   : 230.1   Mean   :  224.4   Mean   :  40.02   Mean   : 215.0  
##  3rd Qu.: 219.0   3rd Qu.:   23.0   3rd Qu.:   4.00   3rd Qu.: 184.8  
##  Max.   :4710.0   Max.   :20990.0   Max.   :3424.00   Max.   :4710.0  
##    num_loves         num_wows         num_hahas           num_sads      
##  Min.   :  0.00   Min.   :  0.000   Min.   :  0.0000   Min.   : 0.0000  
##  1st Qu.:  0.00   1st Qu.:  0.000   1st Qu.:  0.0000   1st Qu.: 0.0000  
##  Median :  0.00   Median :  0.000   Median :  0.0000   Median : 0.0000  
##  Mean   : 12.73   Mean   :  1.289   Mean   :  0.6965   Mean   : 0.2437  
##  3rd Qu.:  3.00   3rd Qu.:  0.000   3rd Qu.:  0.0000   3rd Qu.: 0.0000  
##  Max.   :657.00   Max.   :278.000   Max.   :157.0000   Max.   :51.0000  
##    num_angrys     
##  Min.   : 0.0000  
##  1st Qu.: 0.0000  
##  Median : 0.0000  
##  Mean   : 0.1132  
##  3rd Qu.: 0.0000  
##  Max.   :31.0000

new <- data
new = subset(new, select = -c(Column1, Column2, Column3, Column4) )
new <- separate(new, status_id, into = c("seller_id", "user_id") , sep =  "_" )
new <- separate(new, status_published, into =c("date", "time"), sep = " ")
new

new %>% summarise_if(is.numeric, mean)

new %>% summarise_if(is.numeric, max)

pos_neg <- subset(new, select = c( status_type , num_wows, num_hahas, num_sads, num_angrys, num_loves ))
pos_neg$positive <- pos_neg$num_wows + pos_neg$num_hahas + pos_neg$num_loves
pos_neg$negative <- pos_neg$num_sads + pos_neg$num_angrys

out3 <- pos_neg %>% 
  group_by(status_type) %>% 
  summarise(positive = mean(positive), negative = mean(negative))

## `summarise()` ungrouping output (override with `.groups` argument)

plot_1 <- ggplot(data=out3, aes(x= status_type, y= positive)) +
  geom_bar(stat="identity", fill="purple") +
  geom_text(aes(label=positive), vjust=-0.3, size=3.5)+
  theme_minimal()

pos_neg <- subset(new, select = c( status_type , num_likes , num_wows, num_hahas, num_sads, num_angrys, num_loves ))
pos_neg$positive <-pos_neg$num_likes + pos_neg$num_wows + pos_neg$num_hahas + pos_neg$num_loves
pos_neg$negative <- pos_neg$num_sads + pos_neg$num_angrys

pos_neg

out3 <- pos_neg %>% 
  group_by(status_type) %>% 
  summarise(positive = mean(positive), negative = mean(negative))

## `summarise()` ungrouping output (override with `.groups` argument)

plot_2 <- ggplot(data=out3, aes(x= status_type, y= positive)) +
  geom_bar(stat="identity", fill="steelblue") +
  geom_text(aes(label=positive), vjust=-0.3, size=3.5)+
  theme_minimal()

plot_3 <- ggplot(data=out3, aes(x= status_type, y=negative)) +
  geom_bar(stat="identity", fill="maroon") +
  geom_text(aes(label=negative), vjust=-0.3, size=3.5)+
  theme_minimal()

library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

grid.arrange(plot_1, plot_2, plot_3 ,ncol =3)

new$engagements <- new$num_likes + new$num_comments + new$num_shares
# aggregate(new$engagements, by=list(Status=new$status_type), FUN=sum)
out <- new %>% 
  group_by(status_type) %>% 
  summarise(engagements = mean(engagements))

## `summarise()` ungrouping output (override with `.groups` argument)

out<-arrange(out, engagements )
out

ggplot(data=out, aes(x= status_type, y=engagements)) +
  geom_bar(stat="identity", fill="steelblue")+
  geom_text(aes(label=engagements), vjust=-0.3, size=3.5)+
  theme_minimal()

clean = subset(new, select = -c( num_wows, num_hahas, num_sads, num_angrys, num_loves ))
clean

status_count <- clean %>%
  group_by(status_type) %>%
    summarise_if(is.numeric,  mean)

status_count

per_seller <- clean %>%
  group_by(seller_id) %>%
    summarise_if(is.numeric, sum) 
per_seller <- arrange(per_seller, seller_id)
per_seller

by_time <- clean %>%
  group_by(seller_id, date, time) %>%
    summarise_if(is.numeric, sum) 
by_time <- arrange(by_time, seller_id, date, time)
by_time

ggplot(data = by_time , aes(x = as.Date(date, "%m/%d/%Y") , y = num_reactions)) +
  geom_point( size = 1) +
  geom_line(color = "indianred3", 
            size=1 ) +
  geom_smooth() +
  scale_x_date(breaks = '1 year', 
               labels = date_format("%Y-%m-%d")) +
  labs(title = "All seller reactions",
       subtitle = "Yearly rate",
       x = "Date",
       y = "Num-reactions") +
#ylim(0, 8000) +
  theme_minimal()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

ggplot(data = by_time , aes(x = as.Date(date, "%m/%d/%Y") , y = num_comments)) +
  geom_point( size = 1) +
  geom_line(color = "indianred3", 
            size=1 ) +
  geom_smooth() +
  scale_x_date(date_breaks = '1 year', 
               labels = date_format("%Y-%m-%d")) +
  labs(title = "All seller comments",
       subtitle = "Yearly rate",
       x = "Date",
       y = "Num-comments") +
 # ylim(0, 10000) +
  theme_minimal()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

ggplot(data = by_time , aes(x = as.Date(date, "%m/%d/%Y") , y = num_shares)) +
  geom_point( size = 1) +
  geom_line(color = "indianred3", 
            size=1 ) +
  geom_smooth() +
  scale_x_date(date_breaks = '1 year', 
               labels = date_format("%Y-%m-%d")) +
  labs(title = "All seller shares received",
       subtitle = "Yearly rate",
       x = "Date",
       y = "Num-shares") +
  #ylim(0, 3000) +
  theme_minimal()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

ggplot(data = by_time , aes(x = as.Date(date, "%m/%d/%Y")  , y = num_comments)) +
  geom_point(aes(shape = seller_id, color = seller_id), size = 1) +
  scale_x_date(date_breaks = '1 year', 
               labels = date_format("%Y-%m-%d")) 

## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 9. Consider
## specifying shapes manually if you must have them.

## Warning: Removed 749 rows containing missing values (geom_point).

new_copy <- clean
new_copy$date <- as.Date(new_copy$date, "%m/%d/%Y")
new_copy 

before_live = filter(new_copy , date < "2016-06-04")
after_live = filter(new_copy , date >= "2016-06-04")
on_live_day = filter(new_copy , date == "2016-06-04")
before_live

by_time <- before_live %>%
  group_by(seller_id, date, time) %>%
    summarise_if(is.numeric, sum) 
by_time <- arrange(by_time, seller_id, date, time)
by_time

after_live

pairs(~ num_likes + num_reactions + num_comments+ num_shares , data= clean,
      lower.panel = panel.smooth)

pairs(~  num_reactions + num_comments+ num_shares , data= before_live,
      lower.panel = panel.smooth)

pairs(~  num_reactions + num_comments+ num_shares , data= clean,
      lower.panel = panel.smooth)

ggplot(data = clean, aes(x = num_reactions , y = num_likes )) +
  geom_point() + geom_smooth(method = "lm", se = 0) + 
  xlab("Number of reactions") + 
  ylab("Number of likes")

## `geom_smooth()` using formula 'y ~ x'

ggplot(data = clean, aes(x = num_reactions , y = num_shares )) +
  geom_point() + geom_smooth(method = "lm", se = 0) + 
  xlab("Number of reactions") + 
  ylab("Number of shares")

## `geom_smooth()` using formula 'y ~ x'

barplot(height=status_count$num_likes, names=status_count$status_type, 
        col="#69b3a2",
        horiz=T, las=1
        )

split_date <- clean
split_date$day <- factor(wday(as.Date(split_date$date, "%m/%d/%Y") -1))
split_date$day_date <- factor(day(as.Date(split_date$date, "%m/%d/%Y")))
split_date$month <- factor(format(as.Date(split_date$date, "%m/%d/%Y"),"%m"))
split_date$time_hour <- factor(format(strptime(split_date$time,"%H:%M"),"%H"))
glimpse(split_date)

## Rows: 7,050
## Columns: 14
## $ seller_id     <chr> "246675545449582", "246675545449582", "246675545449582"…
## $ user_id       <chr> "1649696485147474", "1649426988507757", "16487305885773…
## $ status_type   <chr> "video", "photo", "video", "photo", "photo", "photo", "…
## $ date          <chr> "4/22/2018", "4/21/2018", "4/21/2018", "4/21/2018", "4/…
## $ time          <chr> "6:00", "22:45", "6:17", "2:29", "3:22", "2:14", "0:24"…
## $ num_reactions <int> 529, 150, 227, 111, 213, 217, 503, 295, 203, 170, 210, …
## $ num_comments  <int> 512, 0, 236, 0, 0, 6, 614, 453, 1, 9, 2, 4, 4, 4, 11, 1…
## $ num_shares    <int> 262, 0, 57, 0, 0, 0, 72, 53, 0, 1, 3, 0, 2, 0, 0, 30, 7…
## $ num_likes     <int> 432, 150, 204, 111, 204, 211, 418, 260, 198, 167, 202, …
## $ engagements   <int> 1206, 150, 497, 111, 204, 217, 1104, 766, 199, 177, 207…
## $ day           <fct> 7, 6, 6, 6, 3, 3, 3, 2, 2, 3, 2, 1, 7, 7, 4, 7, 5, 1, 5…
## $ day_date      <fct> 22, 21, 21, 21, 18, 18, 18, 17, 17, 11, 10, 9, 8, 8, 5,…
## $ month         <fct> 04, 04, 04, 04, 04, 04, 04, 04, 04, 04, 04, 04, 04, 04,…
## $ time_hour     <fct> 06, 22, 06, 02, 03, 02, 00, 07, 03, 04, 01, 02, 05, 02,…

pairs(~  engagements + time_hour + day_date + month + num_shares + num_comments , data= split_date,
      lower.panel = panel.smooth)

out2 <- split_date %>% 
  group_by(time_hour) %>% 
  summarise(engagements = mean(engagements))

## `summarise()` ungrouping output (override with `.groups` argument)

out2<-arrange(out2, engagements )
out2

ggplot(data=out2, aes(x= time_hour, y=engagements)) +
  geom_bar(stat="identity", fill="steelblue")+
  geom_text(aes(label=engagements), vjust=-0.3, size=3.5)+
  theme_minimal()

library(fastDummies)
split_date <- dummy_cols(split_date, select_columns = c('day', 'month', 'day_date', 'status_type', 'time_hour') )
split_date

set.seed(1234)

library(caTools)
trainIndex <- sample.split( split_date$engagements , SplitRatio = 0.7, group = NULL )

# Create separate training and test set records:
trainingSet <- split_date[trainIndex,]
testSet <- split_date[!trainIndex,]

# Assess the components (based on eigenvalues)
fa.parallel(trainingSet[,6:10], fa="pc", n.iter=100, show.legend=FALSE, main="Scree plot with parallel analysis")

## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done

## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done

## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done

## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done

## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.

## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully

## In factor.scores, the correlation matrix is singular, an approximation is used

## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done

## Parallel analysis suggests that the number of factors =  NA  and the number of components =  2

# Perform PCA, and derive the rotated components
pc <- principal(trainingSet[,6:10], nfactors=3, rotate="varimax", score=TRUE)

## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done

## Warning in principal(trainingSet[, 6:10], nfactors = 3, rotate = "varimax", :
## The matrix is not positive semi-definite, scores found from Structure loadings

pc

## Principal Components Analysis
## Call: principal(r = trainingSet[, 6:10], nfactors = 3, rotate = "varimax", 
##     scores = TRUE)
## Standardized loadings (pattern matrix) based upon correlation matrix
##                 RC2  RC1  RC3 h2      u2 com
## num_reactions  0.98 0.13 0.12  1 1.1e-03 1.1
## num_comments  -0.01 0.96 0.27  1 3.2e-05 1.2
## num_shares     0.10 0.42 0.90  1 4.5e-06 1.4
## num_likes      0.99 0.10 0.04  1 8.8e-04 1.0
## engagements    0.38 0.86 0.34  1 4.7e-05 1.7
## 
##                        RC2  RC1  RC3
## SS loadings           2.11 1.87 1.02
## Proportion Var        0.42 0.37 0.20
## Cumulative Var        0.42 0.80 1.00
## Proportion Explained  0.42 0.38 0.20
## Cumulative Proportion 0.42 0.80 1.00
## 
## Mean item complexity =  1.3
## Test of the hypothesis that 3 components are sufficient.
## 
## The root mean square of the residuals (RMSR) is  0 
##  with the empirical chi square  0.01  with prob <  NA 
## 
## Fit based upon off diagonal values = 1

# List the scoring formulas for the rotated components
round(pc$weights, 2) 

## 
## Loadings:
##               RC2   RC1   RC3  
## num_reactions  0.98  0.13  0.12
## num_comments         0.96  0.27
## num_shares     0.10  0.42  0.90
## num_likes      0.99  0.10      
## engagements    0.38  0.86  0.34
## 
##                  RC2   RC1   RC3
## SS loadings    2.095 1.864 1.015
## Proportion Var 0.419 0.373 0.203
## Cumulative Var 0.419 0.792 0.995

trainingSet <- cbind(trainingSet, pc$scores)

# Generate rotated component scores for testSet
testSet_RCscores <- predict(pc, testSet[,6:10], trainingSet[,6:10])
testSet_RCscores

##               RC2          RC1          RC3
## 6    -0.249021916 -0.633535291 -0.461300444
## 8     0.203018949  0.340041159  0.147628545
## 9    -0.309314440 -0.657061527 -0.471827852
## 15    0.304746052 -0.479457736 -0.384348067
## 21   -0.187752216 -0.601912178 -0.442605491
## 25   -0.744221558 -0.773797115 -0.530868987
## 26   -1.022793323 -0.844327641 -0.562111253
## 30   -0.019821800 -0.023561915 -0.094118332
## 31   -0.823330010 -0.795414625 -0.541591195
## 38   -0.815547171 -0.728688275 -0.451907567
## 40   -0.376546560 -0.139944054 -0.164801671
## 46    0.518022025  0.376136874  0.091273294
## 48   -0.631125309 -0.618683714 -0.417956475
## 49   -0.314627411 -0.153618319 -0.071439417
## 50    0.197645112  0.264531887  0.035516988
## 55   -0.425475471 -0.328042014 -0.121753931
## 56   -0.241103524 -0.007191497 -0.042162382
## 59   -0.753454154 -0.602495227 -0.392921226
## 68    0.480060753  0.538039354  0.398308699
## 71   -0.938664248 -0.825388153 -0.557058300
## 75   -0.130701930  0.040860782 -0.026718671
## 79    0.042000601  0.158956403  0.066169087
## 80   -0.914130865 -0.817043209 -0.553237313
## 83   -0.553415703 -0.330837305 -0.165860460
## 87    0.055828430 -0.552137059 -0.414269039
## 89    0.131978905 -0.529679789 -0.408406939
## 93    1.698264607 -0.114347204 -0.197471675
## 95    1.278015820 -0.214033647 -0.250888973
## 102  -1.029729620 -0.832973704 -0.564027779
## 106  -0.549110983 -0.719964472 -0.504048631
## 107  -0.482602502 -0.665376660 -0.414544642
## 108  -0.369693788 -0.578093496 -0.286707734
## 111   1.201758183 -0.235128430 -0.261572460
## 112  -0.312784494 -0.598286141 -0.395383513
## 114  -0.285671600 -0.358263627 -0.255155124
## 123  -0.719192582 -0.761587361 -0.520345126
## 124  -0.762276450 -0.770642014 -0.530849558
## 125   0.058676222 -0.178276756 -0.170168401
## 126   0.202297423 -0.342577589 -0.235213219
## 130   0.579779848  0.957971699  0.325096019
## 134  -1.112163445 -0.852892773 -0.574601556
## 135  -1.055304381 -0.837730207 -0.566935100
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## 137  -1.148303104 -0.868697254 -0.581415091
## 141  -0.120183420  0.195415435  0.001854009
## 147   0.186546061 -0.521011331 -0.403280090
## 150   0.297564858 -0.036967614 -0.102451632
## 151   1.602429118 -0.132930511 -0.208686739
## 153   0.351636759  0.277891304  0.046911284
## 154   0.282955395 -0.497342064 -0.391139715
## 155  -0.411802058 -0.402372572 -0.254099565
## 156  -0.320726501 -0.661907417 -0.473879657
## 158  -1.011006844 -0.833887200 -0.563416164
## 161  -0.543724986 -0.704982523 -0.483163130
## 162  -0.534953663 -0.675563773 -0.478595112
## 164  -0.553212741 -0.674381497 -0.447792951
## 169  -0.390568924 -0.648392243 -0.473453521
## 170  -0.053866567 -0.559804345 -0.422266947
## 174  -0.091255830 -0.574266777 -0.434345760
## 180  -0.659634164 -0.710098761 -0.469804255
## 181  -1.001109829 -0.825080827 -0.560140993
## 183  -0.948911906 -0.812703030 -0.553588193
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## 1730  1.168691615 -0.235257326 -0.258864680
## 1733 -1.128445799 -0.874194421 -0.582133957
## 1736 -0.971277296 -0.833824193 -0.561427698
## 1737 -1.181218455 -0.889809054 -0.589734615
## 1738 -1.058465412 -0.855532801 -0.572698318
## 1747 -0.861358209 -0.801428576 -0.545636655
## 1748 -1.150602036 -0.881644595 -0.585606523
## 1749 -1.176844681 -0.888642703 -0.589144887
## 1750 -0.610328884 -0.725236048 -0.508878618
## 1751 -1.044769357 -0.848796912 -0.569881278
## 1752 -1.123102762 -0.869535303 -0.575151055
## 1753 -0.785201842 -0.755848546 -0.506436888
## 1756 -0.758930042 -0.766254477 -0.524107963
## 1761 -0.738030433 -0.763915488 -0.527552500
## 1770 -0.837083244 -0.784011965 -0.533675199
## 1772 -1.017377246 -0.835325134 -0.564247197
## 1777 -0.930476496 -0.815234944 -0.553500507
## 1779 -1.166158608 -0.879324466 -0.575179084
## 1780 -1.061977087 -0.851843899 -0.571716258
## 1781 -1.167127870 -0.882817233 -0.581572258
## 1782 -0.962529747 -0.831491491 -0.560248243
## 1786 -0.949121058 -0.826374019 -0.557955132
## 1787 -0.900147443 -0.808688903 -0.549896344
## 1789 -0.996657841 -0.835967048 -0.563394276
## 1797 -0.912406666 -0.807332703 -0.550093739
## 1802 -0.890825162 -0.803119365 -0.547669031
## 1803 -1.097829380 -0.866029962 -0.578005865
## 1805 -0.929219867 -0.810123759 -0.546583404
## 1811 -1.084420691 -0.860912491 -0.575712753
## 1812 -1.101341054 -0.862341061 -0.577023805
## 1818 -0.684395678 -0.743445602 -0.518380056
## 1824 -0.958155973 -0.830325140 -0.559658516
## 1835 -0.761336137 -0.777839333 -0.533120782
## 1842 -1.058752779 -0.857151219 -0.573222247
## 1844 -0.839839982 -0.774105324 -0.535942740
## 1845 -0.932731542 -0.806434857 -0.545601344
## 1864 -1.035914646 -0.847826695 -0.563880436
## 1868 -0.760474037 -0.772984080 -0.531548995
## 1869 -1.079472183 -0.856509304 -0.574075168
## 1872 -0.606242476 -0.725688115 -0.508812820
## 1873 -0.838627239 -0.790741567 -0.541116231
## 1877 -0.982567257 -0.828975227 -0.555231920
## 1881 -0.276485216 -0.628501264 -0.461439690
## 1882 -0.724909111 -0.760416434 -0.525783318
## 1885 -0.990272502 -0.823471773 -0.559137046
## 1889 -1.071874101 -0.860650273 -0.574991429
## 1892 -0.853248671 -0.779222796 -0.538235851
## 1893 -1.092773710 -0.862989262 -0.571546892
## 1896 -1.070149902 -0.850939767 -0.571847855
## 1900 -0.882077614 -0.800786662 -0.546489576
## 1902 -1.092593506 -0.860008358 -0.575844350
## 1914 -0.901296909 -0.815162573 -0.551992060
## 1917 -0.925527989 -0.810831757 -0.551862922
## 1924 -0.731070361 -0.748183378 -0.522247413
## 1925 -0.983823885 -0.834086412 -0.562149023
## 1926 -0.857909811 -0.782007565 -0.539349508
## 1927 -1.076247875 -0.861816624 -0.575581156
## 1933 -0.931625962 -0.821708614 -0.555596223
## 1935 -0.966041422 -0.827802589 -0.559266184
## 1937 -0.890250429 -0.799882529 -0.546621173
## 1942 -1.163723358 -0.885143649 -0.587375705
## 1945 -0.871893233 -0.790361871 -0.542690477
## 1946 -0.952632733 -0.822685118 -0.556973073
## 1948 -1.049430497 -0.851581681 -0.570994934
## 1949 -0.988772392 -0.838489598 -0.563786608
## 1951 -1.169957649 -0.877253982 -0.574720954
## 1959 -0.953782199 -0.829158788 -0.559068789
## 1961 -0.816471002 -0.783291393 -0.537643665
## 1962 -0.801912847 -0.771700251 -0.533254838
## 1963 -1.071874101 -0.860650273 -0.574991429
## 1966 -0.884040327 -0.778669994 -0.502916812
## 1968 -1.124359391 -0.874646488 -0.582068158
## 1969 -1.106864294 -0.869981083 -0.579709248
## 1972 -1.051217973 -0.838182273 -0.566869301
## 1974 -1.070617472 -0.855539088 -0.568074326
## 1978 -1.040395583 -0.847630561 -0.569291550
## 1979 -0.990272502 -0.823471773 -0.559137046
## 1981 -1.053516905 -0.851129614 -0.571060733
## 1982 -1.036883908 -0.851319462 -0.570273610
## 1987 -1.106002195 -0.865125830 -0.578137462
## 1988 -0.940724153 -0.802549821 -0.550030400
## 1989 -0.948546325 -0.823137184 -0.556907274
## 1990 -0.889388330 -0.795027276 -0.545049386
## 1995 -0.790228357 -0.776293286 -0.534105300
## 1999 -0.979450111 -0.832920061 -0.561559295
## 2004 -1.161210101 -0.874921279 -0.573541499
## 2005 -1.137480713 -0.878145541 -0.583837340
## 2017 -1.181218455 -0.889809054 -0.589734615
## 2020 -0.983823885 -0.834086412 -0.562149023
## 2021 -0.852898027 -0.800714292 -0.544981129
## 2025 -0.787310806 -0.738361681 -0.516775417
## 2026 -1.054379005 -0.855984867 -0.572632519
## 2027 -1.133106939 -0.876979190 -0.583247613
## 2032 -1.053804272 -0.852748032 -0.571584661
## 2033 -0.930476496 -0.815234944 -0.553500507
## 2034 -1.039635680 -0.829714658 -0.537391478
## 2035 -0.974214237 -0.826898456 -0.559397781
## 2039 -0.999020050 -0.825804476 -0.560316501
## 2042 -0.980024844 -0.836156896 -0.562607153
## 2053 -1.016515146 -0.830469881 -0.562675411
## 2056 -1.119698250 -0.871861719 -0.580954502
## 2057 -1.092593506 -0.860008358 -0.575844350
## 2058 -1.001318982 -0.838751817 -0.564507932
## 2059 -1.133106939 -0.876979190 -0.583247613
## 2062 -1.045344090 -0.852033747 -0.570929136
## 2063 -1.117686685 -0.860532795 -0.577286999
## 2068 -1.004255923 -0.831826080 -0.562478015
## 2070 -0.973639504 -0.823661621 -0.558349923
## 2074 -1.070437269 -0.852558184 -0.572371784
## 2076 -1.083845958 -0.857675656 -0.574664896
## 2081 -1.016515146 -0.830469881 -0.562675411
## 2082 -0.961443558 -0.801907907 -0.550883321
## 2083 -1.058465412 -0.855532801 -0.572698318
## 2089 -1.117749962 -0.837422882 -0.570017793
## 2091 -1.105203372 -0.837160663 -0.569296468
## 2095 -1.092306139 -0.858389941 -0.575320421
## 2100 -1.080334283 -0.861364557 -0.575646955
## 2105 -1.065776128 -0.849773415 -0.571258128
## 2118 -1.045694733 -0.830542251 -0.564183858
## 2131 -1.063477196 -0.836826074 -0.567066697
## 2132 -1.039533483 -0.842775308 -0.567719763
## 2133 -1.065201395 -0.846536580 -0.570210270
## 2138 -1.054379005 -0.855984867 -0.572632519
## 2141 -1.069862536 -0.849321349 -0.571323926
## 2142 -1.113025544 -0.857748026 -0.576173343
## 2143 -0.978588012 -0.828064808 -0.559987508
## 2144 -1.113600277 -0.860984862 -0.577221201
## 2148 -1.078322718 -0.850035634 -0.571979452
## 2149 -1.088219732 -0.858842007 -0.575254623
## 2150 -1.123784658 -0.871409652 -0.581020300
## 2151 -1.074265465 -0.833083661 -0.550704214
## 2159 -1.137193347 -0.876527124 -0.583313411
## 2164 -1.111238068 -0.871147434 -0.580298976
## 2167 -1.051894903 -0.828358459 -0.537588874
## 2175 -1.046844199 -0.837015922 -0.566279574
## 2185 -1.106576928 -0.868362665 -0.579185319
## 2188 -1.091794683 -0.832043192 -0.567003357
## 2190 -1.072000655 -0.814430445 -0.560453016
## 2196 -1.096392547 -0.857937874 -0.575386220
## 2206 -1.123390129 -0.871153720 -0.575674984
## 2210 -1.114773932 -0.838356329 -0.522957804
## 2214 -1.053409742 -0.852492100 -0.566239345
## 2223 -1.133106939 -0.876979190 -0.583247613
## 2224 -1.089081831 -0.863697260 -0.576826410
## 2226 -1.109513870 -0.861436928 -0.577155402
## 2228 -1.115037110 -0.869076950 -0.579840845
## 2237 -1.181218455 -0.889809054 -0.589734615
## 2239 -1.082983858 -0.852820403 -0.573093109
## 2240 -1.110950702 -0.869529016 -0.579775047
## 2246 -1.025325972 -0.809692670 -0.556585659
## 2247 -1.168097132 -0.886310000 -0.587965432
## 2249 -1.135756514 -0.868435036 -0.580693767
## 2254 -1.135756514 -0.868435036 -0.580693767
## 2255 -1.117749962 -0.837422882 -0.570017793
## 2260 -1.136905980 -0.874908706 -0.582789482
## 2261 -1.087357632 -0.853986754 -0.573682836
## 2263 -1.122815396 -0.867916885 -0.574627126
## 2264 -1.036372452 -0.824972713 -0.561956545
## 2275 -1.133106939 -0.876979190 -0.583247613
## 2276 -1.114175010 -0.864221697 -0.578269059
## 2281 -1.026699527 -0.840894672 -0.566474510
## 2282 -1.084995424 -0.864149326 -0.576760611
## 2288 -1.122133500 -0.866042535 -0.568757881
## 2291 -1.050930607 -0.836563856 -0.566345372
## 2293 -1.140417655 -0.871219804 -0.581807423
## 2304 -1.051505340 -0.839800691 -0.567393230
## 2305 -1.122060459 -0.861699146 -0.577876727
## 2311 -1.131382740 -0.867268684 -0.580104039
## 2312 -1.135756514 -0.868435036 -0.580693767
## 2317 -1.123784658 -0.871409652 -0.581020300
## 2318 -1.114749743 -0.867458532 -0.579316916
## 2320 -1.085920800 -0.845894666 -0.571063191
## 2321 -1.106864294 -0.869981083 -0.579709248
## 2325 -1.114749743 -0.867458532 -0.579316916
## 2327 -1.074811043 -0.853724535 -0.572961512
## 2328 -1.131670107 -0.868887102 -0.580627968
## 2329 -1.106289561 -0.866744247 -0.578661391
## 2330 -1.099042122 -0.849393719 -0.572832374
## 2332 -1.081834392 -0.846346732 -0.570997393
## 2333 -1.123497291 -0.869791235 -0.580496371
## 2340 -1.093455605 -0.864863611 -0.577416137
## 2343 -1.131670107 -0.868887102 -0.580627968
## 2344 -1.132244840 -0.872123937 -0.581675826
## 2345 -1.086208166 -0.847513083 -0.571587120
## 2348 -1.078108392 -0.852760605 -0.562336678
## 2349 -1.112738178 -0.856129609 -0.575649414
## 2357 -1.082482166 -0.853926956 -0.562926405
## 2359 -1.088219732 -0.858842007 -0.575254623
## 2374 -1.137193347 -0.876527124 -0.583313411
## 2379 -1.119985617 -0.873480136 -0.581478431
## 2381 -1.093455605 -0.864863611 -0.577416137
## 2382 -1.123497291 -0.869791235 -0.580496371
## 2391 -1.099616855 -0.852630555 -0.573880232
## 2403 -1.092880872 -0.861626776 -0.576368279
## 2409 -1.110375969 -0.866292181 -0.578727189
## 2411 -1.110375969 -0.866292181 -0.578727189
## 2412 -1.015940413 -0.827233045 -0.561627553
## 2413 -1.106289561 -0.866744247 -0.578661391
## 2417 -1.133106939 -0.876979190 -0.583247613
## 2425 -1.113205748 -0.860728930 -0.571875884
## 2426 -1.070213179 -0.827829853 -0.564578649
## 2428 -1.128733165 -0.875812839 -0.582657885
## 2431 -1.125348044 -0.833281913 -0.569101532
## 2432 -1.120121935 -0.854713612 -0.565090378
## 2433 -1.089325312 -0.843568250 -0.565259745
## 2434 -1.089719841 -0.843824182 -0.570605061
## 2435 -1.061689720 -0.850225482 -0.571192329
## 2436 -1.123390129 -0.871153720 -0.575674984
## 2439 -1.119410884 -0.870243301 -0.580430573
## 2448 -1.083982276 -0.838909131 -0.558276843
## 2451 -1.065488762 -0.848154998 -0.570734199
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## 5898 -1.006648007 -0.843778191 -0.566252632
## 5902 -1.122362763 -0.874374906 -0.581826852
## 5910 -0.550307916 -0.180038243 -0.064933615
## 5911 -1.069877473 -0.860378691 -0.574750123
## 5914 -0.671235398 -0.259693654 -0.031324528
## 5915 -1.106576928 -0.868362665 -0.579185319
## 5916 -1.163723358 -0.885143649 -0.587375705
## 5919 -1.119985617 -0.873480136 -0.581478431
## 5923 -1.106864294 -0.869981083 -0.579709248
## 5928 -1.102490520 -0.868814731 -0.579119521
## 5933 -1.111238068 -0.871147434 -0.580298976
## 5935 -1.154975810 -0.882810947 -0.586196250
## 5942 -1.111238068 -0.871147434 -0.580298976
## 5946 -1.111238068 -0.871147434 -0.580298976
## 5952 -1.058178046 -0.853914383 -0.572174389
## 5957 -1.032315919 -0.847539463 -0.568743139
## 5959 -0.991602171 -0.832926347 -0.556935303
## 5966 -1.079077654 -0.856253372 -0.568729852
## 5976 -1.110663335 -0.867910599 -0.579251118
## 5978 -1.041257682 -0.852485814 -0.570863337
## 5983  0.464461035  2.578691412  2.580984413
## 5992 -1.066243698 -0.854372736 -0.567484599
## 6002 -1.102490520 -0.868814731 -0.579119521
## 6006 -1.010066530 -0.841084519 -0.565687387
## 6016 -0.093265629  0.786816551  1.348473655
## 6018 -1.119123517 -0.868624883 -0.579906644
## 6021 -1.172470907 -0.887476352 -0.588555160
## 6030 -1.124359391 -0.874646488 -0.582068158
## 6033 -1.075960509 -0.860198206 -0.575057227
## 6042 -1.137480713 -0.878145541 -0.583837340
## 6047  0.095685951  2.310923277  2.301239556
## 6048 -1.058752779 -0.857151219 -0.573222247
## 6051 -0.301989324  0.358405851  1.071847873
## 6055 -1.128733165 -0.875812839 -0.582657885
## 6058  0.375305728  2.612382246  2.714696643
## 6060 -1.040970316 -0.850867396 -0.570339408
## 6062 -1.074251247 -0.861545042 -0.575339850
## 6065 -1.098116746 -0.867648380 -0.578529794
## 6069 -0.970127830 -0.827350523 -0.559331983
## 6072 -1.124072024 -0.873028070 -0.581544229
## 6077 -1.102490520 -0.868814731 -0.579119521
## 6094 -0.448607758  0.016887547  0.503711375
## 6095 -1.093742972 -0.866482029 -0.577940066
## 6098 -1.172470907 -0.887476352 -0.588555160
## 6101 -1.080621649 -0.862982975 -0.576170884
## 6104 -1.163148625 -0.881906814 -0.586327847
## 6108 -1.133106939 -0.876979190 -0.583247613
## 6109 -0.434900408  0.035370820  0.333358063
## 6111 -1.141854488 -0.879311893 -0.584427068
## 6118 -1.042937995 -0.840448892 -0.561916317
## 6120 -1.119016355 -0.869987369 -0.575085257
## 6122 -1.185592229 -0.890975405 -0.590324342
## 6124 -1.106864294 -0.869981083 -0.579709248
## 6125 -0.362642593  0.337031756  0.925617413
## 6128 -1.106002195 -0.865125830 -0.578137462
## 6131 -1.132819573 -0.875360773 -0.582723684
## 6134 -1.084995424 -0.864149326 -0.576760611
## 6138  0.369139911  2.146468040  2.136453958
## 6140 -1.146228262 -0.880478244 -0.585016795
## 6141 -1.159349584 -0.883977298 -0.586785977
## 6147 -0.256182885  0.337678157  0.433862610
## 6148 -1.128733165 -0.875812839 -0.582657885
## 6149 -1.087372570 -0.865044096 -0.577109032
## 6155  0.320105820  1.816007574  1.844786760
## 6157 -0.104853356  0.511237069  1.158911951
## 6158 -1.091746344 -0.866210447 -0.577698760
## 6160 -1.061689720 -0.850225482 -0.571192329
## 6161 -1.124359391 -0.874646488 -0.582068158
## 6169 -0.462595894  0.061798443  0.274901059
## 6171  0.415304756 -0.429265599 -0.346593994
## 6177  0.377855368 -0.418495427 -0.314564470
## 6178 -0.696856141 -0.751156284 -0.521170019
## 6185  4.937960236  0.814637126  0.285174162
## 6188  7.302729693  1.583341788  0.675598519
## 6193 10.367159036  2.692410179  1.184849808
## 6195  4.356913312  0.755338294  0.258341834
## 6196  2.535476622  0.286640649  0.028328344
## 6198 17.521139003  4.124937701  1.960320140
## 6199 16.698279762  4.032501280  1.888577138
## 6206  4.283963438  0.928109677  0.308780066
## 6209 -0.820715689 -0.781583377 -0.526399432
## 6210  1.501201427 -0.009629181 -0.096693952
## 6212 -0.819365909 -0.777467422 -0.519899143
## 6220  7.470859751  1.539223795  0.726612369
## 6221  3.129012981  0.498904777  0.288255751
## 6223 -0.078654083 -0.573624456 -0.412794177
## 6224  6.231095630  1.123951607  0.438133214
## 6226  0.176769357 -0.469755538 -0.375154039
## 6227 -0.700936449 -0.755675582 -0.517771987
## 6232 -0.204371202 -0.602089586 -0.438154412
## 6234  5.894524173  1.047813549  0.397091142
## 6238  0.093203720 -0.524033649 -0.409362979
## 6240  6.383637182  1.383453210  0.532860930
## 6241  6.015712903  1.108792944  0.427858212
## 6248  7.483605974  1.473572424  0.601653613
## 6253  2.333863031  0.295834986  0.009816985
## 6254  1.021246909 -0.276622573 -0.268411932
## 6262  9.370411067  2.087299484  0.975038858
## 6263 -0.819365909 -0.777467422 -0.519899143
## 6268  2.695291685  0.453418850  0.125390417
## 6278 -0.632994320 -0.553986579 -0.418887678
## 6282 -0.079924467  2.556126004  1.581131871
## 6284 -1.040488734 -0.846635330 -0.568874736
## 6291 -1.149632774 -0.878151828 -0.579213348
## 6294 -1.004744530 -0.842511379 -0.565594512
## 6299 -1.157411060 -0.876991763 -0.573999629
## 6302 -1.031453819 -0.842684210 -0.567171352
## 6304  0.242312772  2.954932290  2.737485082
## 6311 -0.867101154 -0.761853541 -0.533366871
## 6315 -0.967649620 -0.822846875 -0.557626004
## 6319 -0.836377779 -0.726957442 -0.494543554
## 6320 -1.026520249 -0.849338365 -0.568959963
## 6339 -1.036962122 -0.839266890 -0.566430599
## 6340 -1.124310539 -0.841200896 -0.534827892
## 6341 -0.905417645 -0.780910507 -0.536124254
## 6344 -1.037824221 -0.844122143 -0.568002386
## 6346 -0.580472452 -0.411216870 -0.067364430
## 6348 -1.074631765 -0.862168229 -0.575446965
## 6352 -0.768067735 -0.696899723 -0.507884372
## 6353 -0.613294506 -0.609256274 -0.325680770
## 6356 -0.938470033 -0.822774505 -0.556117557
## 6357 -0.977804126 -0.811156983 -0.554572711
## 6370  1.014104744  4.810622990  4.367617693
## 6373 -0.701482509 -0.592270133 -0.434747251
## 6374 -1.109241440 -0.870875852 -0.580057669
## 6378 -0.804052757 -0.256340202 -0.192324510
## 6382 -1.054472156 -0.854989637 -0.572215705
## 6386  0.534046133  3.265747638  2.669120010
## 6397 -1.132912724 -0.874365542 -0.582306870
## 6404 -0.802677410 -0.705607345 -0.512495076
## 6406 -0.861290547 -0.752595101 -0.530157499
## 6408 -0.892355146 -0.810216223 -0.549871862
## 6409 -0.980511844 -0.771861725 -0.437451001
## 6416 -0.926259150 -0.789963449 -0.545224759
## 6422 -0.995903830 -0.841173906 -0.564831871
## 6425 -1.049143131 -0.849963263 -0.570471005
## 6428 -0.827753050 -0.773838318 -0.529673515
## 6437 -0.884215733 -0.765895758 -0.535618666
## 6438 -0.035981288 -0.258072584 -0.332516395
## 6439 -0.932293846 -0.823950219 -0.556227266
## 6440 -0.840522803 -0.787404271 -0.539999980
## 6443 -1.004997981 -0.821741546 -0.522304370
## 6451 -1.154975810 -0.882810947 -0.586196250
## 6455 -1.115611843 -0.872313785 -0.580888703
## 6456 -0.760047101 -0.701519270 -0.492978913
## 6460 -1.091804448 -0.859496494 -0.565153718
## 6462 -0.756674108 -0.776903688 -0.531884578
## 6463 -0.861910092 -0.789003961 -0.541483945
## 6465 -0.142681040 -0.424568327 -0.326540084
## 6467 -1.120925930 -0.866282817 -0.579207207
## 6469 -0.738313415 -0.693590517 -0.505328066
## 6474 -0.898702698 -0.795992706 -0.545502327
## 6476 -0.676402178 -0.749179763 -0.513828453
## 6493 -1.067500327 -0.859483921 -0.574401702
## 6504 -0.049951456 -0.282275717 -0.256658173
## 6507 -0.954601545 -0.795597085 -0.516864139
## 6510 -0.813067622 -0.768948281 -0.511761269
## 6511 -1.067212960 -0.857865504 -0.573877773
## 6512 -0.914482434 -0.806976310 -0.544680030
## 6515 -0.988003444 -0.832639115 -0.561798007
## 6521 -1.080621649 -0.862982975 -0.576170884
## 6525 -1.063126553 -0.858317570 -0.573811974
## 6527 -1.013779445 -0.846462499 -0.567297895
## 6532 -1.095594650 -0.841397305 -0.564733221
## 6533 -1.003408761 -0.838028169 -0.564332425
## 6537 -1.101521258 -0.865321964 -0.572726347
## 6541 -1.123390129 -0.871153720 -0.575674984
## 6543 -1.138313864 -0.872310708 -0.576744729
## 6544 -1.041638200 -0.853109001 -0.570970452
## 6546 -1.028136360 -0.848986760 -0.569094155
## 6548 -1.087932365 -0.857223589 -0.574730694
## 6554 -0.415478716 -0.429464787 -0.379225063
## 6557 -1.157907785 -0.864187045 -0.549016660
## 6562  1.569675067  8.242868310  5.426569429
## 6575 -1.122362763 -0.874374906 -0.581826852
## 6579 -0.669439022 -0.603466889 -0.426149875
## 6581 -0.614754037  0.625286037  0.482587534
## 6588 -0.735330171  0.546187967  0.257449442
## 6590 -1.059191400 -0.851060453 -0.560784320
## 6592 -1.084816628 -0.688546697 -0.473964030
## 6595 -0.468350640  0.860550871  0.454155065
## 6596 -0.489380230  1.058261215  0.510707081
## 6597 -1.080621649 -0.862982975 -0.576170884
## 6602 -0.743142578  0.122952071 -0.058182851
## 6610 -0.567324155 -0.715553421 -0.492719170
## 6613 -0.954147780 -0.818724634 -0.555749707
## 6621 -0.669766333 -0.750360265 -0.519486063
## 6627 -0.701574666 -0.707708378 -0.481676801
## 6629 -0.789832014 -0.766461881 -0.530449442
## 6632 -1.084995424 -0.864149326 -0.576760611
## 6634 -0.989596705 -0.816626060 -0.556731631
## 6643 -1.070904839 -0.857157505 -0.568598255
## 6645 -0.828913068 -0.719425941 -0.512447064
## 6648 -0.979450111 -0.832920061 -0.561559295
## 6649  0.247676392  4.003747410  1.832326115
## 6650 -0.941952759 -0.808395517 -0.546471100
## 6652 -0.978480849 -0.829427293 -0.555166121
## 6656 -0.070671955 -0.289001426 -0.327848073
## 6657 -0.977739962 -0.836116021 -0.561719371
## 6658 -0.815911206 -0.791111900 -0.540022003
## 6661 -1.084708057 -0.862530909 -0.576236682
## 6671 -0.345988543 -0.459158063 -0.401090300
## 6673 -0.246849951 -0.518179872 -0.382822925
## 6675 -0.776353023 -0.778001090 -0.533773713
## 6677  0.467187564  0.174547149 -0.112902971
## 6678  0.076192362 -0.050430731 -0.219342480
## 6683  0.027617317 -0.067586349 -0.185094274
## 6685 -0.980965158 -0.828959577 -0.560335930
## 6691 -1.067500327 -0.859483921 -0.574401702
## 6692 -1.119016355 -0.869987369 -0.575085257
## 6700 -0.839776705 -0.797215238 -0.543211947
## 6703 -0.874852137 -0.800946709 -0.545738580
## 6704 -0.870486275 -0.804384466 -0.546923224
## 6708 -0.261165138 -0.617512872 -0.456714086
## 6710 -0.638500947 -0.603446861 -0.335408636
## 6714 -0.546121317 -0.689114076 -0.494025588
## 6717 -0.920687570 -0.816490682 -0.553234718
## 6722  0.314537717 -0.066922441 -0.180766232
## 6725 -0.971277296 -0.833824193 -0.561427698
## 6726 -0.655977127 -0.744619606 -0.517085838
## 6729 -0.904565310 -0.803508557 -0.532702828
## 6739  0.013596314 -0.130753573 -0.226935845
## 6740 -0.035614945  2.210332458  0.875971507
## 6749 -0.599576795 -0.718027655 -0.505683485
## 6759  0.940975560  0.346368460 -0.060608266
## 6760  0.484259282  0.647547724  0.099858809
## 6766 -0.086466491 -0.211278425 -0.314791450
## 6769  0.211577539  0.036904502 -0.216054807
## 6781 -0.565509492  0.724825756  0.218555197
## 6783 -0.658914068 -0.737693869 -0.515055920
## 6793 -0.401748095 -0.625754816 -0.465815271
## 6802  0.279977889 -0.195928819 -0.285571304
## 6807  0.264639138  0.232938944 -0.136146035
## 6816 -0.655092177 -0.724102903 -0.510313483
## 6817 -0.811343217 -0.787331900 -0.538491533
## 6818  0.225650271  0.291592207 -0.014725643
## 6819 -0.421521976 -0.279952370 -0.358868893
## 6821 -1.002546662 -0.833172916 -0.562760638
## 6830 -0.577176633 -0.535235572 -0.410307556
## 6841 -0.113106818 -0.183344284 -0.260425992
## 6843 -0.931037218 -0.818839034 -0.549310163
## 6847 -0.888419993 -0.802959106 -0.536844207
## 6858 -1.137480713 -0.878145541 -0.583837340
## 6859 -1.141854488 -0.879311893 -0.584427068
## 6861 -0.917793795 -0.805645125 -0.539293397
## 6864 -1.097542013 -0.864411545 -0.577481936
## 6865 -0.810862324 -0.215479296 -0.295141748
## 6870  0.291660592  2.817926662  1.529582163
## 6875 -0.961760799 -0.825641008 -0.558259642
## 6876 -1.084420691 -0.860912491 -0.575712753
## 6877 -1.054817626 -0.849894102 -0.560194592
## 6880 -0.973712546 -0.828005010 -0.549231077
## 6884 -0.803804410 -0.033092054 -0.179695605
## 6888 -0.837527728 -0.193904699 -0.284627995
## 6889 -0.247701003  1.812484773  0.718346046
## 6890 -1.083379313 -0.864500932 -0.576626420
## 6896 -1.003501913 -0.837032938 -0.563915610
## 6898 -1.054550370 -0.842937065 -0.568372695
## 6899 -1.061495505 -0.847611834 -0.570251586
## 6901 -0.821670014 -0.774018802 -0.529366410
## 6912 -1.104867666 -0.869709501 -0.579467942
## 6921 -0.757952867 -0.758157601 -0.515940417
## 6924 -0.268290625  2.039124469  0.542389171
## 6926 -1.028229511 -0.847991530 -0.568677341
## 6927 -1.133106939 -0.876979190 -0.583247613
## 6930 -0.361341411 -0.626298877 -0.422892802
## 6934 -0.901355220 -0.780354495 -0.509573180
## 6935 -0.504036702  0.788364943  0.236218708
## 6943 -1.043619891 -0.842323241 -0.567785562
## 6952 -1.154975810 -0.882810947 -0.586196250
## 6953 -1.106576928 -0.868362665 -0.579185319
## 6957 -1.044633245 -0.839469311 -0.556395493
## 6958 -1.079652387 -0.859490208 -0.569777710
## 6962 -1.020888921 -0.831636232 -0.563265138
## 6967 -1.091229715 -0.856259659 -0.564105860
## 6968 -1.112645952 -0.868549436 -0.574254223
## 6970 -1.128733165 -0.875812839 -0.582657885
## 6973 -1.154006548 -0.879318179 -0.579803076
## 6977 -1.006453792 -0.841164543 -0.565311889
## 6978  1.251853263  7.120860822  2.933259274
## 6983 -1.185592229 -0.890975405 -0.590324342
## 6989 -1.163723358 -0.885143649 -0.587375705
## 6994 -1.151421175 -0.876177017 -0.573275710
## 6996  1.800875243 -0.072220385 -0.143523858
## 7000 -0.300970466 -0.642919337 -0.446082242
## 7004 -0.384228396 -0.669068993 -0.479474880
## 7005  0.039530970 -0.543128202 -0.397288351
## 7007 -0.702538548 -0.755691232 -0.512667977
## 7011 -1.145078796 -0.874004573 -0.582921079
## 7019 -1.084995424 -0.864149326 -0.576760611
## 7022 -0.880606660 -0.798400449 -0.529929699
## 7025 -1.133106939 -0.876979190 -0.583247613
## 7027 -1.146228262 -0.880478244 -0.585016795
## 7032 -0.966903522 -0.832657842 -0.560837971
## 7035 -1.159349584 -0.883977298 -0.586785977
## 7039 -1.181218455 -0.889809054 -0.589734615
## 7042 -1.150982554 -0.882267783 -0.585713637
## 7043 -1.168097132 -0.886310000 -0.587965432
## 7047 -1.120366135 -0.874103324 -0.581585545
## 7048 -1.179221827 -0.889537472 -0.589493308
## 7049  0.369620370 -0.387113758 -0.237089881
## 7050 -1.111238068 -0.871147434 -0.580298976

testSet <- cbind(testSet,testSet_RCscores)
testSet

#fitted <- lm( num_comments ~  status_type + num_shares + num_reactions , 
#       data= trainingSet, na.action = na.exclude)

fitted <- lm( engagements ~ num_reactions , 
       data= trainingSet, na.action = na.exclude)

summary(fitted)

## 
## Call:
## lm(formula = engagements ~ num_reactions, data = trainingSet, 
##     na.action = na.exclude)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1852.3  -245.9  -221.9  -216.2 20634.8 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   216.17353   17.02574   12.70   <2e-16 ***
## num_reactions   1.35819    0.03078   44.12   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1082 on 5097 degrees of freedom
## Multiple R-squared:  0.2764, Adjusted R-squared:  0.2762 
## F-statistic:  1947 on 1 and 5097 DF,  p-value: < 2.2e-16

par(mfrow=c(2,2))
plot(fitted)

model.regression <- stepAIC(fitted, direction="forward")

## Start:  AIC=71255.07
## engagements ~ num_reactions

model.regression

## 
## Call:
## lm(formula = engagements ~ num_reactions, data = trainingSet, 
##     na.action = na.exclude)
## 
## Coefficients:
##   (Intercept)  num_reactions  
##       216.174          1.358

#drawback on such models on COUNTS data, results are not integers
testSet.predictRegression <- predict(model.regression, testSet)
summary(testSet.predictRegression)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   216.2   237.9   281.4   451.6   458.6  6076.8

Dataset.Predict <- cbind(testSet.predictRegression,
                         testSet$num_comments)
#glimpse(Dataset.Predict)
Dataset.Predict <- as.data.frame(Dataset.Predict)
#glimpse(Dataset.Predict)

names(Dataset.Predict) <- c("MLR","Target") 

ggplot (data = Dataset.Predict) +
  geom_point (mapping = aes (x = Target, 
                             y = MLR-Target ), shape = 21, fill = "red") 

library(MLmetrics)

## 
## Attaching package: 'MLmetrics'

## The following object is masked from 'package:psych':
## 
##     AUC

## The following object is masked from 'package:base':
## 
##     Recall

R2_Score(testSet.predictRegression, testSet$engagements)

## [1] 0.4638593

RMSE(testSet.predictRegression, testSet$engagements)

## [1] 382.4785

MAE(testSet.predictRegression, testSet$engagements)

## [1] 290.5487

#R2_Score(testSet.predictRegression, testSet$num_comments)
#RMSE(testSet.predictRegression, testSet$num_comments)
#MAE(testSet.predictRegression, testSet$num_comments)

library(randomForest)

## randomForest 4.6-14

## Type rfNews() to see new features/changes/bug fixes.

## 
## Attaching package: 'randomForest'

## The following object is masked from 'package:gridExtra':
## 
##     combine

## The following object is masked from 'package:psych':
## 
##     outlier

## The following object is masked from 'package:ggplot2':
## 
##     margin

## The following object is masked from 'package:dplyr':
## 
##     combine

trainingSet_1 = subset(trainingSet, select = -c( engagements, seller_id, user_id, status_type, date, time,  day_date, month, time_hour, day , num_comments, num_shares, num_likes ))
testSet_1 = subset(testSet, select = -c( engagements, seller_id, user_id, status_type, date, time,  day_date, month, time_hour, day, num_comments, num_shares, num_likes))
model_RF <- randomForest(trainingSet_1, trainingSet$engagements , ntree= 300 , mtry= 15 , importance=TRUE)

# Importance of features
round(importance(model_RF), 2)

##                    %IncMSE IncNodePurity
## num_reactions         6.90  8.700846e+08
## day_1                -0.80  1.167405e+07
## day_2                 1.57  5.501528e+06
## day_3                -0.59  1.248511e+07
## day_4                -2.64  4.492804e+06
## day_5                 0.51  8.985483e+06
## day_6                -0.28  6.057806e+06
## day_7                 3.08  7.981946e+06
## month_01              1.22  7.221657e+06
## month_02              3.55  6.420981e+06
## month_03              3.82  5.390216e+06
## month_04              2.94  6.417051e+06
## month_05              3.93  1.969583e+07
## month_06              0.48  5.810320e+06
## month_07              4.19  4.429266e+06
## month_08              5.99  1.217708e+07
## month_09              5.35  6.074685e+07
## month_10              3.70  3.282845e+06
## month_11              3.73  4.348332e+07
## month_12              1.63  1.735612e+07
## day_date_1            0.96  1.122632e+06
## day_date_2            1.52  3.449603e+06
## day_date_3            0.87  2.345366e+06
## day_date_4            0.66  2.637577e+06
## day_date_5           -0.05  2.619702e+06
## day_date_6           -0.90  1.526983e+06
## day_date_7           -1.40  4.063760e+06
## day_date_8            0.23  5.222812e+06
## day_date_9           -1.29  2.228991e+07
## day_date_10          -1.47  2.008091e+06
## day_date_11          -0.31  1.855391e+06
## day_date_12          -0.40  1.795706e+06
## day_date_13          -0.70  2.961523e+06
## day_date_14           0.54  1.744096e+06
## day_date_15           0.56  1.643956e+06
## day_date_16          -1.35  2.105987e+06
## day_date_17           0.57  1.014645e+06
## day_date_18          -1.17  1.973685e+06
## day_date_19          -1.99  2.414417e+06
## day_date_20           0.16  2.153826e+06
## day_date_21          -1.29  5.186580e+06
## day_date_22          -0.62  2.080798e+06
## day_date_23           1.15  2.076775e+06
## day_date_24           0.05  5.094217e+06
## day_date_25          -1.99  2.651283e+07
## day_date_26          -1.44  1.694533e+06
## day_date_27          -0.87  2.986329e+06
## day_date_28           0.29  2.119161e+06
## day_date_29           0.09  1.445584e+06
## day_date_30           0.57  3.871198e+07
## day_date_31          -0.87  1.369833e+06
## status_type_link     -0.16  2.967544e+05
## status_type_photo     5.21  1.393236e+08
## status_type_status    2.34  4.987928e+06
## status_type_video     6.88  1.912205e+08
## time_hour_00          1.46  1.680451e+06
## time_hour_01          2.80  1.138232e+07
## time_hour_02          2.78  4.138548e+06
## time_hour_03          1.86  2.203172e+06
## time_hour_04          1.66  1.406328e+06
## time_hour_05          2.52  4.364614e+06
## time_hour_06          1.32  5.089649e+06
## time_hour_07          2.80  8.494582e+06
## time_hour_08          1.03  4.144169e+07
## time_hour_09          0.38  5.815810e+06
## time_hour_10          2.32  4.189220e+07
## time_hour_11          0.75  2.557686e+06
## time_hour_12          1.05  8.108224e+05
## time_hour_13         -1.62  1.368849e+05
## time_hour_14          0.89  2.546498e+04
## time_hour_15          1.28  6.003099e+04
## time_hour_16         -1.00  2.999200e+03
## time_hour_17          0.31  3.268000e+01
## time_hour_18          1.00  3.721000e+01
## time_hour_19          1.61  9.568690e+04
## time_hour_20         -1.08  1.625086e+05
## time_hour_21          2.61  3.805771e+07
## time_hour_22          1.96  1.980796e+06
## time_hour_23         -0.51  2.809815e+06
## RC2                   7.08  1.172623e+09
## RC1                  15.62  3.021154e+09
## RC3                  14.08  2.135301e+09

varImpPlot(model_RF,  n.var=10)

testSet.predictRF <- predict(model_RF, testSet_1)

R2_Score(testSet.predictRF, testSet$engagements)

## [1] 0.9869683

RMSE(testSet.predictRF, testSet$engagements)

## [1] 59.63057

MAE(testSet.predictRF, testSet$engagements)

## [1] 16.54485

library(e1071)

model_svm <- svm(trainingSet_1, trainingSet$engagements, kernel="linear")

model_svm <- svm(trainingSet_1, trainingSet$engagements, kernel="linear")

# Make prediction
testSet.predictSVM <- predict(model_svm, testSet_1)

R2_Score(testSet.predictSVM, testSet$engagements)

## [1] 0.9913074

RMSE(testSet.predictSVM, testSet$engagements)

## [1] 48.7014

MAE(testSet.predictSVM, testSet$engagements)

## [1] 38.0316

# cbind predicted value vectors into a dataset
Dataset.Predict <- cbind(testSet.predictRegression,
                      #   testSet.predictMLP,
                         testSet.predictSVM,
                         testSet.predictRF,
                         testSet$engagements)

# Convert matrix to data frame
Dataset.Predict <- as.data.frame(Dataset.Predict)
names(Dataset.Predict) <- c("REG", "SVM", "RF","Target")  # add header names

# Plot residual (i.e. prediction errors) vs. target values
ggplot (data = Dataset.Predict) +
   geom_point (mapping = aes (x = Target, 
                             y = REG-Target), shape = 21, fill = "red") +
  #geom_point (mapping = aes (x = Target, 
                            # y = MLP-Target), shape = 21, fill = "yellow") +
  geom_point (mapping = aes (x = Target, 
                           y = SVM-Target), shape = 21, fill = "green") +
  geom_point (mapping = aes (x = Target, 
                           y = RF-Target), shape = 21, fill = "blue")